You can read that post (it is a good one :) for more details, but the essence of the phenomenon is that your website most likely has a "thick head" (say ten or fifteen keywords that drive massive number of Visits) and usually a "long tail" (hundreds of thousands of key words and key phrases that drive five, ten, fifteen – few – Visits).

As an example for the month of Jan '09 for my blog:

the "head" is 11 keywords (100 or more Visits) and

the "tail" is, and I am always astonished by this, 22,181 key words!

Can you believe that?

For such a deeply focused blog on one topic there were a total of 22k key phrases that delivered traffic from search engines.

That's both a reflection of all the love/work I put into SEO and a real living breathing example of the long tail.

My long tail post also outlines how the head and the tail have very unique characteristics when it comes to the kinds of:

key words that are present in the head (usually brand keywords) and the tail (usually category / generic / ecosystem) and

people / visitors that use who come with head (people who know you already / have made up their mind) and tail (people new to your franchisee) key words and key phrases

Read the post on the search head and long tail to see how to uniquely use Organic Search (SEO) and Paid Search (SEM / PPC) strategically to plan your world domination.

My recommendation there was that most businesses should have a very focused and efficient long tail search strategy to ensure they keep attracting newbies (prospects) to their company / product / franchise. These people have not made up their mind. Find them first, make them fall in love with you, retain them.

[Bringing the story back to where we started….]

"Upper Funnel" has a very brotherly correlation to the "long tail" keywords. Both introduce new people to you, people who have not yet decided what they want to buy / sign up for etc, people who will take a little bit to buy.

So what's the problem? Why don't more people do this?

Mental Model & Measurement.

It turns out that most Online Marketers tend to think of life, online atleast, to be all about "one night stands". Come. Wallet out. Convert. I have addressed this issue a lot, most recently in my Measure Latent Conversions & Visitor Behavior post.

The "single session" mindset is very corrosive. Mostly because typical customer behavior looks like this:

People are introduced to your firm / products / services. Great. They are early in the consideration life cycle. They might go away. Come back again. Perhaps now with a branded search query. Go away. Then some will come back and convert later.

You on the other hand are measuring single session conversion.

That translates into you devaluing all the "upper funnel" / "long tail" keywords as losers.

For example I placed an order for Mobile Madden 09 at www.eamobile.com by clicking on search ad at www.google.com for Madden 09. But the reality it I only considered EA because a couple days back I had clicked on an ad for Mobile Football Games on google.com.

Early stage / upper funnel / long tail keywords are significantly cheaper, let's hold them to a lower standard.

"Purchase stage" life cycle products can be expensive. Let's hold them to a much much higher standard.

Here's how your potential measurement strategy would look like:

Use Bounce Rate to measure early customer consideration lifecycle keywords. You are paying 30 or 60 (or whatever) cents per click, getting that traffic to come and stay on your site (give you one pathetic click) might be a worthy goal. They came, they saw. Rather than they came, they puked, they left (my definition of bounce rate ).

For the next set of keywords (active consideration) there is a lot more competition. They are more expensive. Demand that those keywords drive traffic that spends a lot of time on your site. Explore what you have to offer, get to second base .

Then for your high cost brand keywords expect that they will bring visitors that will show a high degree of Visitor Loyalty, i.e they will come for multiple visits, getting very so close to converting (third base! ).

You may pick different metrics, your customer life cycle might be longer / shorter, your keyword portfolio might be fatter or leaner than mine. Consider this my attempt at sharing a adaptable framework.

The Outcome:

You have put together a flexible framework that helps value your paid search keyword portfolio in a significantly better way.

You still demand that every keyword you bid on delivers its pound of flesh (value).

You also map things back to the customer consideration life cycle, achieving better targeting and landing page experience on your site.

Sweetness.

Think of this contrast: You just "blew" $3 million on a 30 second ad at the Super Bowl. The ad was irrelevant to most people who saw it (or heard it as they ran to the bathroom). Now contrast that with executing the above strategy with Microsoft, Yahoo!, Google, for 30 or 40 or low cents per click (or even dollars) getting in front of relevant people, those that actually typed in keywords related to your business, people who are looking for you! A hugely higher ROI awaits you.

Go!

Epilogue:

You should be able to do much of the above with your Web Analytics tools. Especially if you unleash the power of data segmentation .

If you work with Search Agencies then demand that their tools do this for you. They can. You may have to push. Do it.

Doubleclick has such a report, "exposure to conversion" report. It is ok, not as sexy cool as it could be. Atlas has something similar as well. ClickEquations, for Paid Search, has something close, you should check it out. [Disclosure : I am on their Advisory Board.]

A couple web analytics vendors, in exchange for $1 to $2 mil, will give you additional addons on top of the web analytics tool you are paying for. These addons don't have multi touch attribution analysis reports built in. But if you want to you could built it yourself (though you'll have to come up with your own attribution models).

It is my hope that we as an industry will slowly move towards allowing you much easier access to really advanced features like multi touch attribution analysis.

It is a very tough problem to solve. For example it is computationally very intensive (and expensive), and you have no idea how hideously complex attribution models can be.

But I hope we'll get there.

It is important to realize you don't have to wait for that. In this post I am not requiring multi touch attribution analysis. You can do what I am suggesting tomorrow.

You can start to reshape your approach and mental model today.

Good luck.

Ok now your turn.

Do you have a long tail? Do you see the behavior I have outlined above on your site? How do you value your upper funnel keywords? Do you think my model will work?

Comments

Hi Avinash,
Good post. We use a model like this and acknowledge that the keywords that bring visitors to our site don't have the best conversion rate. But they are *brilliant* at attracting people to our site. We view the long tail as a big fishing net – the more effective we can be at SEO on the long tail, the bigger our fishing net and the more (new) visitors we catch. We look at bounce rates and time on site to determine how well we are doing here.

As we imprint awareness into the minds of these customers, they come back but add 'brand terms' to their searches, and this is when conversion takes off. The only 'head terms' on our website are the brand terms, i.e. terms with our company name in them. Every single other keyword (70k in a month) reside in the long tail.

With this strategy it is imperative that we convince the business that focusing on the apparently less-well converting long tail terms is crucial to successfully building a base of visitors that arrive via 'direct' or 'brand term' sources. Manage that, and you're well on your way to building a strong foundation for the future.

There is a single metric I really want to build: 'of the visitors that come to my site via a non-brand term, how many return in the following 30 days using a brand term'? This would be fantastic. I'm working on a keyword report that uses advanced segments and keyword filtering to achieve this – but I agree with Avinash – multi-touch attribution capability (in GA) would be fantastic.

I like this model, especially for SEO where it's a bit easier not to be held to strict ROI standards (it's easier when money isn't going out the door).

I'd like to add two ideas to the mix:

1.) For paid search, you'll either need to organize and label your categorize your campaigns by consideration phase or apply meta data to be able to roll up these data easily into a report with those metrics.

2.) For SEO, you'll probably have to work with your vendor to create rules tools to auto-group organic search words to support this kind of analysis. I would actually also suggest you do this by organic search landing page as well. Sometimes the words you attract aren't the culprit :-)

And… thanks for the ClickEquations shout out :-) Shameless plug: if you organize your campaigns properly, you can easily do this analysis with ClickEquations Analyst, our Excel plug-in http://www.clickequations.com/analyst/

Our long tail is a lot shorter but has helped us understand what our customers are looking for in great ways. I love that you preach the short tail as many people would dismiss it. Thanks for another great post on this subject!

This is such an interesting and "open" issue. I had 2 meetings with prospects today where this was certainly one of the vague issues they are struggling with (both prospects).
I must disagree to some extent with the model you're suggesting (different KPIs for different stages on the funnel). I think KPIs should change based on the landing pages' goal and even KW's goal – not by the stage/category.

I think the direction to the conversion attribution concept should be assigning a portion of the conversion value to visits which later on convert.

For example:

If someone searched for “hotels” and went to mytravelsite.com, browsed around, kept shopping around, then 10 days later searches for “ophir’s boutique hotel in heaven”, clicked again to mytravelsite.com (perhaps different landing page) and converted, the conversion should be split between the keyword “travel” and the keyword “ophir’s boutique hotel in heaven”.

The split proportion can be set wither by the analytics tool automatically, or manually by the account administrator and consider # of visits to conversion and time between first click and conversion. The analytics tools all use cookies, and are able to save both last visit and first visit. IMO it’s only a matter of pulling the data and presenting it.
Similarly, even if the Analytics tools won’t analyze each clicks exact attribution, simply reporting keywords which users searches BEFORE they converted – may be a huge leap IMO.

The current models will never “prove” that head terms show positive ROI, even though they most probably do. We know for a fact that when removing head terms everything goes down even if long tail traffic stays the same, similarly to offline activities such as TV campaigns.

I sure would love to see GA offer this capability sometime in the near future.

Great post. One other method to look at is segmentation based on Geo locations. It is quite possible that long tail keywords attract "lurkers" from geo locations that are not target audience and not likely to convert. Of course looking at bounce in combination with time on site will help you weed out some and increase on investment on others. Doing Geo targeting on long tail keywords or completely weeding out some can help. Life Time Value analysis can do wonders, but it is resource (process and people) intensive.

Tools can and should bring greater clarity to this issue for advertisers.

As an SEM we study this data on behalf of our clients, mostly retailers, and have found that this type of funnel behavior is much less common than often proported. For our clients multi-click interactions take place prior to fewer than 20% of orders, and that's with a very long 45 day window. Of those 20% roughly a quarter go from a competitive phrase to brand, a quarter go from one competitive phrase to another (but as often from specific to general as the other way around, and most often just variations) and the other quarter of the time they're unrelated to each other, with the last phrase being related to the products ordered.

Studying the data is important, but to a very good approximation we find in the retail space that if it appears, based on last touch, that a PPC keyword converts poorly, it almost invariably converts poorly all things considered as well.

I remember seeing a post by Justin at EpikOne where he referenced a custom piece of script that they put together which would track (through the User Defined section) the series of "touches" that a visitor goes through. Have you seen this at all? I'm considering contracting them and/or other developers to do just this, because I agree with you that this kind of functionality is sadly lacking and extremely valuable. I have very few clients that wouldn't benefit from gaining easier insight into the full, mult-touch experiences of their visitors.

Appreciate this analysis. This is an approach we communicate to clients at Mediasmith on the value of engaging their customer at each step of the consideration process.

In my opinion a good analogy is to look at the classic retail world. If the first time a prospective customer saw your brand on the shelf was when they went to the store you do not have as good a chance to sell your product as the other advertisers who have been engaging them everywhere else.

In the same way if you only bid on those search terms that are product or brand level searches than you are missing where a lot of the contact takes place — higher in the funnel.

We have put some serious thought into this issue and I think that as far as paid search goes that we have solved your problem.

To cope with this situation you described above we created a Premium Path to Conversion tool which allows search marketers to set the conversion attribution as well as quickly identify all clicks a customer makes before a conversion.

In your example above about the eamobile game, once you clicked the first ad we would begin tracking you as a customer. And we do this cross channel so that if you searched in google, then yahoo then back to google we would recognize you as a single user. We solved the problem by allowing search marketers to share the value of the conversion among all of the click that lead up to final conversion. In your eamobile example both of clicks would share the value of the conversion as set by the search marketer. If someone wanted to build a campaign that focused on driving traffic they could set the conversion attribution to be more weighted to the “upper funnel”. We did this to allow the greatest possible flexibility when setting campaign goals.

This post (and the previous post – "Measure Latent Conversions & Visitor Behavior") are great inspirations for some Advanced Segments in Google Analytics. It's very neat to apply an Advanced Segment that shows Visits with Conversions (a "default" Advanced Segment), and then create one that, for example, shows Visits with 2+ Conversions, Visits with 3+ Conversions, Visits with 2+ Conversions AND 10+ Pageviews, etc… It really allows you to analyze things in a much different dimension (like the 3D glasses you had to use for the Super Bowl commercials).

I am often surprised at how "engaged" (sorry!) converted visitors are with a website, and whether any of these conversions are credited to Upper Funnel KWs (or Branding or Lower Funnel or etc…).

Good series of posts about this not-so-talked-about topic!

P.S. Really off-topic and probably irrelevant question: Why are the timestamps of blog comments on Mountain Time Zone? I posted at 2PM EST and I was fully expecting to see an 11AM PST timestamp…

Another good post on a topic people miss out on. Reminds me of the classic story: Two shoe salesmen went to Africa individually to explore sales opportunities. After witnessing the local conditions, one person called his office: “I can’t sell shoes here. Nobody wears shoes here.” The other person was very excited and dialed his office: “I can’t believe what I am seeing. Send me ten thousand pairs of shoes immediately! Everyone is barefooted here!”.

The relevancy of this story here is that everyone sees the long-tail. However, not everyone sees it as an opportunity.

In terms of tracking the effectiveness of media buy driving the search (ppc + natural). Generally speaking, what percentage of search traffic comes from banners/outbound email blasts. We are in process of implementing a solution similar to double click and want to know if spending extra on this solution is justifiable.

I totally agree with the value of doing this – what is very frustrating though, is omnitures lack of ability to calculate bounce rate by keyword. I have suggested this to them many times to make this an 'out of the box' report – they simply just say they have a 'plugin' that will work, but now keep getting told that they plugin has issues which may compromise other reporting if we install it. Very frustrating, particuarly when GA offers this as a standard report. Your thoughts? What is going to make omniture finally see the value of this?

About the conversion keywords. I don't think you should judge keywords on their performance in a certain period, but you should look at the uplift (or downfall) of these keywords in a certain period, compared to their lifetime performance. You can also compare the performance uplift (or downfall) to the performances of all your other keywords. Beleve me, this gives you some extra, very interesting insights!!

I think Kpis should change based on the landing pages' goal and even KW's goal – not by the stage/category.

That's exactly what I am saying in the post. Change the kpi based on what the goal of the keyword is. Some are for introducing people to your brand (lightest laptop in the world) and others are for immediate conversion (thinkpad x301).

:)

I agree with you that if you have build a model to do multi touch attribution analysis (or as you phrase it, assign a portion of the conversion value) then it would be optimal. It is much much harder and complex than it appears, so if you can do it then you should totally go bonkers with it!

Rahul: Great ideas.

One way in the past I have attempted to "weed out" "lurkers" was to segment by loyalty and then for the people with highest loyalty if I can't detect a conversion event than it points to lurkers. Not that there's anything wrong with lurkers, they are more than welcome on the site but probably not through campaigns were I am spending money (search or display or email or whatever).

George: The great thing about the framework, and of course what you do, is that it is measurable. In my own humble experience it is difficult to come up with global rules because each business is unique. But we can measure our hypothesis and use it if it is valid, if not we do something else. Its not like we work in the "faith based initiatives" world of TV ads! :)

I must rush to point out that the mental model I had outlined is not unique for Paid Search. It could be used with email, affiliates, display or other types of campaigns. Or across all those channels (this was specifically what I was alluding to with the Epilogue in the post).

Sam: If there is anyone who can give you an answer to your question, then they are lying. There is no "right answer". It depends on who you are, what you are trying to accomplish, where your audience exists.

What you are looking to do is experiment with different media mix models and figure out what works best for you.

If you are working with an Agency then ask them to do it for you. If you are doing this by yourself then you should do it yourself. Doing controlled experiments with various mix models will get you the right answer.

Rich: As you can imagine I am not very well place to answer about Omniture's strategy.

I do know that they are very responsive to their customers so please do make the case to them. I am positive that they will build a plugin to compute bounce rate by keyword as a standard report.

This model only works for certain industries and then completely flips for others. For example, in your post you state that "Upper funnel keywords are typically those that are used by Customers who are early in their consideration life cycle, they have not made up their mind." However lets put this in the context of the real estate industry. So I think we would all agree that the word "real estate" is a "Head" term, and something like "Las vegas condos for sale" is part of the long tail. I would say that someone who is searching for this longtail keyword is actually further along in the buying process as they have allready made up their mind that they are not just searching for real estate in general but are now looking specifically for condos for sale in a specific area. So from this point of view the upper funnel keywords in this industry represent consumers who are much further along in the buying process and as a result the upper funnel keywords should be measured most against conversions and bounce rate, and the head terms should be valued most against "visitor loyalty" and time on site.

What do you think..

p.s. Thanks for all the great, well written & thought provoking posts. I'm a fan of this blog!

This is a great post. The core ideas and concepts/issues that this brings up are central to SEM, and specifically relevant to SEM for retail.

Your post brings up one of the most fundamental questions in SEM – "Are all KWs created equal?", or shall we more clearly state, "Should all KWs be viewed, managed, optimized, measured, and assigned value in an equal manner"?

The model you presented clearly says "NO!!!!"

The model you presented gives relative value to groups of KWs that perform differently. One then assigns specific KPI metrics for each grouping. If bounce rate and AVG Time on Site are used as the KPI for the Top of the Funnel KW groupings, then these KWs are given the room to drive quality traffic and increase both the width of the top of the funnel and the complete tunnel flow. PERFECT!

This is the ideal way that an SEM optimizer would like to look at these top of the funnel KWs.

I work on the client side, for a Consumer Reports 2008 Top 10 e-retail site (only clue I will give). We have divided are KW portfolio by buy cycle in a similar fashion as your suggested model. Our issue has been getting management to "buy in" to marketing spend that has its core metric as a non conversion KPI. The spend on these KW groups is seen as a potential risk, if there can't be a direct line drawn to the exact return on this spend. This leads to the question, "How does one quantify the return on this spend?"

If one is able to access data for multi-click attribution then great! As your post stated clearly, this view is a bit ahead of the current curve for most of the SEM industry. Even on a simplistic level, if one is able to identify 1st click data for a visitor, then one can attribute a sales value to a lead generating KW that may correspond to a different or many different conversion KWs. This can create a view that shows a return value and gives greater insight into the nature of this return for these top of the funnel KWs. This insight drifts out of the strict CPA models and daily return on ad spend that many e-retailers are held to, and allows for a more Long Term Return Value or Indirect Return Value to be applied. This value of return can be looked at on more of a bi-monthly frequency and to give even greater insight, can be combined in a composite weighted score that incorporates both metrics like AVG Time on Site and bounce rate, with straight up ROAS or Cost of Sales metrics. Then management can be given a view with the following understanding: We are driving quality traffic and sessions at the top of our funnel allowing KWs to be optimized towards quality based non conversion metrics, yet we are keeping this KW group in line with ROAS and Cost over Sale metrics. The best of both worlds.

There are several other topics that your post brings to mind, but I've already pushed limit of the long-winded response.

Hi Avinash, I like your "translate this post" app that you got from nothing2hide.net It's kind of ironic because the translated version doesn't make any sense LOL (at least in Russian) and they are not hiding it either. I think it breaks as soon as it runs into your first sarcastic comment.

Alex: I am pretty sure the translation is not as good as it can be. Especially, as you point out, when I am making inside jokes or using sarcasm for effect (which I do!). But my hope is to provide something with the thought that something's better than nothing.

Great post on this subject. I would also recommend segmenting by region (if national), isolating different locations (west coast, east coast, farm land, etc…)to pick up on regional up and down trends – we've seen huge improvements in conversions for some areas by doing this.

Of course, this assumes that your site is performing well to begin with. If it's not, then you'll just be spinning around like a dog chasing its tail….

A long time ago we did recommend using GA's custom segmentation feature to track multiple touch points. However, as we learned more about the custom segment attribution model we started to shy away from this technique.

We're currently working on a new technique, that uses a non-GA cookie, to track multiple touch points. I hope to blog about it soon.

Avinash, as always an insightful, and actionable, post. I really like how you change the focus of the metrics and data to match the most important thing, the customer.

I feel that marketers should pay more attention to each stage of the buying cycle. Measuring performance at each stage can be difficult if you haven't constructed your campaign accordingly and set up correct success metrix.

I go into more detail in my presentation titled 'Measuring PPC-campaign performance', which can be found at bit.ly/5nZmNu

I'm not clear how the ROI will work on this…branded key words are less expensive while non branded are pricier. If you shift spend from branded terms (b/c we'll optimize for them via SEO) to the non branded tail, you'll be paying a higher CPC for lower converting terms.

Adam: Some main non-branded terms are expensive (say "digital camera" etc). But most long tail non-branded terms won't be. The point is not to obsess about one term that might get 5000 clicks but 5000 terms that will get 15000 clicks and be profitable for you.

If you find that you are paying higher CPC's or that they don't convert or that they convert but are not profitable (even if there is revenue) then you stop buying those terms.

Let the data make the decision rather than an opinion that x is true or y is true because a "Industry Analyst" published a study or z is true because a "Guru" said it. :)

Many thanks Avinash for all the information you have published on your blog – I have been working through them steadily and they have helped me greatly.

I have been working with advanced segments to isolate all brand keyword search traffic (including misspellings) – this took a while as I am new to regex. When I applied the brand segment to the search keywords report, it seems to order the data rather than filter – meaning all the relevant brand keywords/phrases and misspellings show up at the top of the report but it also adds a whole lot of other keywords that do not fulfil the regex formula. I tested this using a regex testing site.

What I did notice was that all these keywords have zero visits. I am not sure what I am doing wrong or if this is how it's meant to be?

This is awesome. I am making it a mission to get more into attribution modelling / multi-touch attribution and was curious to know if you have any top of mind resources that can help walk someone like me through a step by step setup of Google's custom variables… It seems like the coolest, most effective way to measure attribution with limited resources. Let me know if you have any suggestions top of mind.

I have been following on google analytics youtube channel for a long time and I have learned a great deal from your videos.
I am facing a small issue which you had once discussed in your video about conditional funnels

Here is my case

Step1: User comes on home page
Step 2: User may log in if he is already registered or might register if he is new user.
Login and registration are 2 diffrent pages
Step 3: the user completes the goal after logging in
On step 2 i can use regular expressions for both urls and create a funnel but from what i remember you had mentioned a tool which does this in a better way. Can you help with link to that too?

I am sorry but I am just not able to find that video in which you discussed this question

Trackbacks

[…]
Avinash Kaushik had a great post this morning about the different types of keywords visitors use to reach your site, what their intentions are, and how to measure keyword effectiveness based on where customers are in the sales funnel. This got me thinking about metrics in general. I often hear people complain that they are not getting enough hits to their website (which is a very outdated metric in any case). But when you ask them what goal/objective they are trying to measure, they usually cannot tie the two together.
[…]

[…] Occam's Razor blog this week made a powerful and very instructionally clear post on how to measure the success of Upper Funnel Keywords and I suggest you read his post for the details on how to do […]

[…] Avinash Kaushik recommends 4 different success metrics for 4 different stages of the buying cycle. What would you measure as a success metric for branded keywords vs. category keywords? Stumped? Read this article. […]

[…] The keyword that a searcher enters should be catagorized according to where they are at in the buying cycle. For example I had a client that sold Appliances. Appliances Direct saw far more conversions at a low cost for the model numbers of there products than at the brand name terms. By identifying what stage of the buying cycle a keyword is in you can modify your bids accordingly and ultimately increase your return. […]

[…]
Being totally dependant on conversion rate is so limiting. Broad, Generic Keywords are doing their purpose by driving traffic to the site – a potential customer we then need to engage, nurture and guide through their decision making process to eventually convert. We choose to keep or chuck a KW based on conversion – so those Traffic driving KW at the top of the funnel totally miss out on any of the glory – it was all down to that Long Tail, specific, model number plus brand KW? I don’t think so. But how do we measure the effectiveness of these very different KW? They have different objectives, give them different measures!! Avinash – thank you!!
[…]

[…] Buying Stage Schizophrenia is when our selling process doesn’t jive with the visitor’s buying process. It’s when our conversion funnel is designed for a buying stage that the visitor isn’t in. Take a look at your site’s conversion funnel…it’s most likely designed for Late Stage buyers, right? Take a look at one of your PPC campaigns…are you showing Early Stage keywords a Middle Stage ad that sends the visitor down a Late Stage funnel? Poor visitor […]

[…]
Last few days I saw some weird new metrics on my Google AdWords report center. At first I didn’t pay too much attention, but then came Google’s message and it got me thinking about the whole concept of conversion attribution models and how easily we can get the wrong conclusions when not analyzing the data properly. I will now present how easily we can persuade ourselves to take wrong campaign actions based on data which may seem very promising.
[…]

[…]
For more on this concept see Avinash Kaushik’s post, Paid Search Analytics: Measuring Value of “Upper Funnel” Keywords.

By leveraging keywords and web site activity that occurs earlier in the sales cycle, Extra Space provides a more reliable stream of conversion data to its SEM team. This consistent conversion stream creates a more accurate picture of future revenue, and conversion rates allowing for smarter keyword bidding.
[…]

[…]
Should I just cut the display ad channel then? Actually I tried that for a while already That gave me a significant lower CPA but unfortunately my SEM-campaigns suddenly got a lot more expensive and the volume in search engines in general fell. Doh!

I have turned on the display campaigns again. I have developed a special excel sheet trying to compensate for all the flaws. I’m measuring my upper-funnel on more parameters that just conversions and all in all trying to compensate. I’m not happy though!
[…]

[…]
The problem is well described by the ever-excellent Avinash Kaushik in his post entitled Measuring Upper Funnel Keywords (although nominally about paid search, his description applies perfectly well to natural search except you aren't paying for traffic in the same way). It can be summarised by thinking about all those reports we have all seen showing branded search terms being the best-converting. While this is true in the sense that the individual finally converted after searching for the brand, it's clearly not the way they found out about your services. For the purposes of setting strategy, you need to understand in better detail your "visitor acquisition" channels that eventually lead to conversions.
[…]

[…]
We’ve long known that people see a lot of different cpc ads during a sales cycle. Avinash Kaushik calls these keywords “upper funnel” keywords. They are used by people that are early in the buying cycle. While many of these keywords don’t always lead to a conversion they help educate a potential customer and move then closer to purchasing a product or service. Even though they do not directly generate revenue there is some value in bidding on upper funnel keywords.
[…]

[…]
We all know the purchase funnel that people go through as they make decisions, here are some great posts about that, so if you are not familiar with the searcher lifecycle, reference these resources by Avinash Kaushik, Marty Weintraub, and Gord Hotchkiss of Enquiro.

[…]
One basic but important aspect of this idea can be seen in the conversion funnel, a term referring to the track a user takes while visiting a website. Here we use conversion to mean a successful purchase, post, or other desired behavior. The value of this data is obvious. By examining the whole of the conversion funnel, community managers can refine their sites to better target the desired demographic.
One important way to understand these conversions is to analyze what are known as long-tail keywords.
[…]

[…]
Now don’t get me wrong. Voice of customer is not necessarily new for companies and brands that use social media and online surveys to get/collect feedback, write content, offer first-line customer service, or calm down one very pi****-off client. Heck, if you are that much of a forward-thinking company/brand, you may even have tried to use voice of customer for your upper funnel keywords for both paid search and your SEO.
[…]